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Hack up de build to use as base-image 🚀
Signed-off-by: Jefferson Fialho <[email protected]>
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‎Dockerfile.ubi‎

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## Global Args #################################################################
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ARG BASE_UBI_IMAGE_TAG=9.4
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ARG PYTHON_VERSION=3.12
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ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
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ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
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## Base Layer ##################################################################
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FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} as base
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ARG PYTHON_VERSION
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ENV PYTHON_VERSION=${PYTHON_VERSION}
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RUN microdnf -y update && microdnf install -y \
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python${PYTHON_VERSION}-pip python${PYTHON_VERSION}-wheel \
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&& microdnf clean all
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WORKDIR /workspace
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ENV LANG=C.UTF-8 \
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LC_ALL=C.UTF-8
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# Some utils for dev purposes - tar required for kubectl cp
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RUN microdnf install -y \
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which procps findutils tar vim git\
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&& microdnf clean all
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## Python Installer ############################################################
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FROM base as python-install
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ARG PYTHON_VERSION
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ENV VIRTUAL_ENV=/opt/vllm
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ENV PATH="$VIRTUAL_ENV/bin:$PATH"
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ENV PYTHON_VERSION=${PYTHON_VERSION}
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RUN microdnf install -y \
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python${PYTHON_VERSION}-devel && \
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python${PYTHON_VERSION} -m venv $VIRTUAL_ENV && pip install --no-cache -U pip wheel uv && microdnf clean all
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## CUDA Base ###################################################################
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FROM python-install as cuda-base
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RUN curl -Lo /etc/yum.repos.d/cuda-rhel9.repo \
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https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
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RUN microdnf install -y \
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cuda-nvcc-12-4 cuda-nvtx-12-4 cuda-libraries-devel-12-4 && \
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microdnf clean all
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ENV CUDA_HOME="/usr/local/cuda" \
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PATH="${CUDA_HOME}/bin:${PATH}" \
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LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${CUDA_HOME}/extras/CUPTI/lib64:${LD_LIBRARY_PATH}"
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## Python cuda base #################################################################
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FROM cuda-base AS python-cuda-base
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ENV VIRTUAL_ENV=/opt/vllm
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ENV PATH="$VIRTUAL_ENV/bin:$PATH"
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# install cuda and common dependencies
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RUN --mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=requirements-common.txt,target=requirements-common.txt \
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--mount=type=bind,source=requirements-cuda.txt,target=requirements-cuda.txt \
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uv pip install \
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-r requirements-cuda.txt
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## Development #################################################################
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FROM python-cuda-base AS dev
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# install build and runtime dependencies
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RUN --mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=requirements-common.txt,target=requirements-common.txt \
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--mount=type=bind,source=requirements-cuda.txt,target=requirements-cuda.txt \
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--mount=type=bind,source=requirements-dev.txt,target=requirements-dev.txt \
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--mount=type=bind,source=requirements-lint.txt,target=requirements-lint.txt \
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--mount=type=bind,source=requirements-test.txt,target=requirements-test.txt \
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uv pip install \
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-r requirements-cuda.txt \
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-r requirements-dev.txt
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## Builder #####################################################################
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FROM dev AS build
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# install build dependencies
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RUN --mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=requirements-build.txt,target=requirements-build.txt \
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uv pip install -r requirements-build.txt
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# install compiler cache to speed up compilation leveraging local or remote caching
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# git is required for the cutlass kernels
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RUN rpm -ivh https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm && rpm -ql epel-release && microdnf install -y git ccache && microdnf clean all
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COPY . .
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ARG TORCH_CUDA_ARCH_LIST
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ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
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ARG vllm_fa_cmake_gpu_arches
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ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
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# max jobs used by Ninja to build extensions
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ARG max_jobs=2
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ENV MAX_JOBS=${max_jobs}
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# number of threads used by nvcc
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ARG nvcc_threads=8
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ENV NVCC_THREADS=$nvcc_threads
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# make sure punica kernels are built (for LoRA)
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ENV VLLM_INSTALL_PUNICA_KERNELS=1
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# Make sure the cuda environment is in the PATH
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ENV PATH=/usr/local/cuda/bin:$PATH
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ENV CCACHE_DIR=/root/.cache/ccache
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RUN --mount=type=cache,target=/root/.cache/ccache \
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--mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,src=.git,target=/workspace/.git \
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env CFLAGS="-march=haswell" \
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CXXFLAGS="$CFLAGS $CXXFLAGS" \
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CMAKE_BUILD_TYPE=Release \
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python3 setup.py bdist_wheel --dist-dir=dist
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#################### libsodium Build IMAGE ####################
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FROM base as libsodium-builder
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RUN microdnf install -y gcc gzip \
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&& microdnf clean all
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WORKDIR /usr/src/libsodium
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ARG LIBSODIUM_VERSION=1.0.20
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RUN curl -LO https://github.com/jedisct1/libsodium/releases/download/${LIBSODIUM_VERSION}-RELEASE/libsodium-${LIBSODIUM_VERSION}.tar.gz \
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&& tar -xzvf libsodium*.tar.gz \
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&& rm -f libsodium*.tar.gz \
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&& mv libsodium*/* ./
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RUN CFLAGS="-O3 -Wall -Werror=format-security -Wno-unused-function -Wp,-D_GLIBCXX_ASSERTIONS -fstack-protector-strong -fstack-clash-protection -fcf-protection"\
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./configure --prefix="/usr/" && make -j $MAX_JOBS && make check
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## Release #####################################################################
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FROM python-install AS vllm-openai
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ARG PYTHON_VERSION
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WORKDIR /workspace
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ENV VIRTUAL_ENV=/opt/vllm
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ENV PATH=$VIRTUAL_ENV/bin/:$PATH
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# force using the python venv's cuda runtime libraries
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ENV LD_LIBRARY_PATH="${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/nvidia/cuda_nvrtc/lib:${LD_LIBRARY_PATH}"
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ENV LD_LIBRARY_PATH="${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/nvidia/cuda_runtime/lib:${LD_LIBRARY_PATH}"
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ENV LD_LIBRARY_PATH="${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/nvidia/nvtx/lib:${LD_LIBRARY_PATH}"
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# Triton needs a CC compiler
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RUN microdnf install -y gcc \
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&& microdnf clean all
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# install vllm wheel first, so that torch etc will be installed
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RUN --mount=type=bind,from=build,src=/workspace/dist,target=/workspace/dist \
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--mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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uv pip install $(echo dist/*.whl)'[tensorizer]' --verbose
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# Install libsodium for Tensorizer encryption
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RUN --mount=type=bind,from=libsodium-builder,src=/usr/src/libsodium,target=/usr/src/libsodium \
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cd /usr/src/libsodium \
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&& make install
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RUN --mount=type=cache,target=/root/.cache/pip \
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--mount=type=cache,target=/root/.cache/uv \
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uv pip install \
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"https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu124torch2.4-cp312-cp312-linux_x86_64.whl"
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ENV HF_HUB_OFFLINE=1 \
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HOME=/home/vllm \
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# Allow requested max length to exceed what is extracted from the
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# config.json
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# see: https://github.com/vllm-project/vllm/pull/7080
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VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
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VLLM_USAGE_SOURCE=production-docker-image \
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VLLM_WORKER_MULTIPROC_METHOD=fork \
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VLLM_NO_USAGE_STATS=1
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# setup non-root user for OpenShift
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RUN umask 002 \
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&& useradd --uid 2000 --gid 0 vllm \
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&& chmod g+rwx $HOME /usr/src /workspace
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COPY LICENSE /licenses/vllm.md
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# Copy only .jinja files from example directory to template directory
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COPY examples/*.jinja /app/data/template/
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USER 2000
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ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
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FROM vllm-openai as vllm-grpc-adapter
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# Start from released image
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FROM quay.io/opendatahub/vllm:cuda-pr-182 as vllm-grpc-adapter
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USER root
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# Copy source code changes into the installed location to overwrite the installed python code
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COPY vllm /opt/vllm/lib64/python3.12/site-packages/vllm
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# RUN --mount=type=cache,target=/root/.cache/pip \
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# pip install vllm-tgis-adapter==0.5.1
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RUN --mount=type=cache,target=/root/.cache/pip \

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